02447nas a2200169 4500008004100000245005000041210004900091260001300140520194800153100002002101700003302121700002102154700002802175700001702203700002002220856003702240 2024 eng d00aComplexity-constrained quantum thermodynamics0 aComplexityconstrained quantum thermodynamics c3/7/20243 a

Quantum complexity measures the difficulty of realizing a quantum process, such as preparing a state or implementing a unitary. We present an approach to quantifying the thermodynamic resources required to implement a process if the process's complexity is restricted. We focus on the prototypical task of information erasure, or Landauer erasure, wherein an n-qubit memory is reset to the all-zero state. We show that the minimum thermodynamic work required to reset an arbitrary state, via a complexity-constrained process, is quantified by the state's complexity entropy. The complexity entropy therefore quantifies a trade-off between the work cost and complexity cost of resetting a state. If the qubits have a nontrivial (but product) Hamiltonian, the optimal work cost is determined by the complexity relative entropy. The complexity entropy quantifies the amount of randomness a system appears to have to a computationally limited observer. Similarly, the complexity relative entropy quantifies such an observer's ability to distinguish two states. We prove elementary properties of the complexity (relative) entropy and determine the complexity entropy's behavior under random circuits. Also, we identify information-theoretic applications of the complexity entropy. The complexity entropy quantifies the resources required for data compression if the compression algorithm must use a restricted number of gates. We further introduce a complexity conditional entropy, which arises naturally in a complexity-constrained variant of information-theoretic decoupling. Assuming that this entropy obeys a conjectured chain rule, we show that the entropy bounds the number of qubits that one can decouple from a reference system, as judged by a computationally bounded referee. Overall, our framework extends the resource-theoretic approach to thermodynamics to integrate a notion of time, as quantified by complexity.

1 aMunson, Anthony1 aKothakonda, Naga, Bhavya Tej1 aHaferkamp, Jonas1 aHalpern, Nicole, Yunger1 aEisert, Jens1 aFaist, Philippe uhttps://arxiv.org/abs/2403.0482802447nas a2200169 4500008004100000245005000041210004900091260001300140520194800153100002002101700003302121700002102154700002802175700001702203700002002220856003702240 2024 eng d00aComplexity-constrained quantum thermodynamics0 aComplexityconstrained quantum thermodynamics c3/7/20243 a

Quantum complexity measures the difficulty of realizing a quantum process, such as preparing a state or implementing a unitary. We present an approach to quantifying the thermodynamic resources required to implement a process if the process's complexity is restricted. We focus on the prototypical task of information erasure, or Landauer erasure, wherein an n-qubit memory is reset to the all-zero state. We show that the minimum thermodynamic work required to reset an arbitrary state, via a complexity-constrained process, is quantified by the state's complexity entropy. The complexity entropy therefore quantifies a trade-off between the work cost and complexity cost of resetting a state. If the qubits have a nontrivial (but product) Hamiltonian, the optimal work cost is determined by the complexity relative entropy. The complexity entropy quantifies the amount of randomness a system appears to have to a computationally limited observer. Similarly, the complexity relative entropy quantifies such an observer's ability to distinguish two states. We prove elementary properties of the complexity (relative) entropy and determine the complexity entropy's behavior under random circuits. Also, we identify information-theoretic applications of the complexity entropy. The complexity entropy quantifies the resources required for data compression if the compression algorithm must use a restricted number of gates. We further introduce a complexity conditional entropy, which arises naturally in a complexity-constrained variant of information-theoretic decoupling. Assuming that this entropy obeys a conjectured chain rule, we show that the entropy bounds the number of qubits that one can decouple from a reference system, as judged by a computationally bounded referee. Overall, our framework extends the resource-theoretic approach to thermodynamics to integrate a notion of time, as quantified by complexity.

1 aMunson, Anthony1 aKothakonda, Naga, Bhavya Tej1 aHaferkamp, Jonas1 aHalpern, Nicole, Yunger1 aEisert, Jens1 aFaist, Philippe uhttps://arxiv.org/abs/2403.0482801537nas a2200157 4500008004100000245004800041210004800089260001400137520103000151100002101181700002001202700002801222700001701250700002801267856008401295 2022 eng d00aLinear growth of quantum circuit complexity0 aLinear growth of quantum circuit complexity c3/28/20223 a

The complexity of quantum states has become a key quantity of interest across various subfields of physics, from quantum computing to the theory of black holes. The evolution of generic quantum systems can be modelled by considering a collection of qubits subjected to sequences of random unitary gates. Here we investigate how the complexity of these random quantum circuits increases by considering how to construct a unitary operation from Haar-random two-qubit quantum gates. Implementing the unitary operation exactly requires a minimal number of gates—this is the operation’s exact circuit complexity. We prove a conjecture that this complexity grows linearly, before saturating when the number of applied gates reaches a threshold that grows exponentially with the number of qubits. Our proof overcomes difficulties in establishing lower bounds for the exact circuit complexity by combining differential topology and elementary algebraic geometry with an inductive construction of Clifford circuits.

1 aHaferkamp, Jonas1 aFaist, Philippe1 aKothakonda, Naga, B. T.1 aEisert, Jens1 aHalpern, Nicole, Yunger uhttps://www.quics.umd.edu/publications/linear-growth-quantum-circuit-complexity01714nas a2200181 4500008004100000245004400041210004400085260001500129490000800144520120900152100002801361700002801389700002101417700002001438700001701458700002001475856003701495 2022 eng d00aResource theory of quantum uncomplexity0 aResource theory of quantum uncomplexity c12/19/20220 v1063 a

Quantum complexity is emerging as a key property of many-body systems, including black holes, topological materials, and early quantum computers. A state's complexity quantifies the number of computational gates required to prepare the state from a simple tensor product. The greater a state's distance from maximal complexity, or "uncomplexity," the more useful the state is as input to a quantum computation. Separately, resource theories -- simple models for agents subject to constraints -- are burgeoning in quantum information theory. We unite the two domains, confirming Brown and Susskind's conjecture that a resource theory of uncomplexity can be defined. The allowed operations, fuzzy operations, are slightly random implementations of two-qubit gates chosen by an agent. We formalize two operational tasks, uncomplexity extraction and expenditure. Their optimal efficiencies depend on an entropy that we engineer to reflect complexity. We also present two monotones, uncomplexity measures that decline monotonically under fuzzy operations, in certain regimes. This work unleashes on many-body complexity the resource-theory toolkit from quantum information theory.

1 aHalpern, Nicole, Yunger1 aKothakonda, Naga, B. T.1 aHaferkamp, Jonas1 aMunson, Anthony1 aEisert, Jens1 aFaist, Philippe uhttps://arxiv.org/abs/2110.1137102255nas a2200205 4500008004100000245005300041210005000094260001300144520167300157100002001830700002001850700002301870700002101893700001601914700002401930700002501954700001701979700001601996856003702012 2022 eng d00aA single T-gate makes distribution learning hard0 asingle Tgate makes distribution learning hard c7/7/20223 a

The task of learning a probability distribution from samples is ubiquitous across the natural sciences. The output distributions of local quantum circuits form a particularly interesting class of distributions, of key importance both to quantum advantage proposals and a variety of quantum machine learning algorithms. In this work, we provide an extensive characterization of the learnability of the output distributions of local quantum circuits. Our first result yields insight into the relationship between the efficient learnability and the efficient simulatability of these distributions. Specifically, we prove that the density modelling problem associated with Clifford circuits can be efficiently solved, while for depth d=nΩ(1) circuits the injection of a single T-gate into the circuit renders this problem hard. This result shows that efficient simulatability does not imply efficient learnability. Our second set of results provides insight into the potential and limitations of quantum generative modelling algorithms. We first show that the generative modelling problem associated with depth d=nΩ(1) local quantum circuits is hard for any learning algorithm, classical or quantum. As a consequence, one cannot use a quantum algorithm to gain a practical advantage for this task. We then show that, for a wide variety of the most practically relevant learning algorithms -- including hybrid-quantum classical algorithms -- even the generative modelling problem associated with depth d=ω(log(n)) Clifford circuits is hard. This result places limitations on the applicability of near-term hybrid quantum-classical generative modelling algorithms.

1 aHinsche, Marcel1 aIoannou, Marios1 aNietner, Alexander1 aHaferkamp, Jonas1 aQuek, Yihui1 aHangleiter, Dominik1 aSeifert, Jean-Pierre1 aEisert, Jens1 aSweke, Ryan uhttps://arxiv.org/abs/2207.0314002430nas a2200205 4500008004100000245007100041210006900112260001500181520180900196100002002005700002002025700002302045700002102068700001602089700002402105700002502129700001702154700001602171856003702187 2021 eng d00aLearnability of the output distributions of local quantum circuits0 aLearnability of the output distributions of local quantum circui c10/11/20213 a

There is currently a large interest in understanding the potential advantages quantum devices can offer for probabilistic modelling. In this work we investigate, within two different oracle models, the probably approximately correct (PAC) learnability of quantum circuit Born machines, i.e., the output distributions of local quantum circuits. We first show a negative result, namely, that the output distributions of super-logarithmic depth Clifford circuits are not sample-efficiently learnable in the statistical query model, i.e., when given query access to empirical expectation values of bounded functions over the sample space. This immediately implies the hardness, for both quantum and classical algorithms, of learning from statistical queries the output distributions of local quantum circuits using any gate set which includes the Clifford group. As many practical generative modelling algorithms use statistical queries -- including those for training quantum circuit Born machines -- our result is broadly applicable and strongly limits the possibility of a meaningful quantum advantage for learning the output distributions of local quantum circuits. As a positive result, we show that in a more powerful oracle model, namely when directly given access to samples, the output distributions of local Clifford circuits are computationally efficiently PAC learnable by a classical learner. Our results are equally applicable to the problems of learning an algorithm for generating samples from the target distribution (generative modelling) and learning an algorithm for evaluating its probabilities (density modelling). They provide the first rigorous insights into the learnability of output distributions of local quantum circuits from the probabilistic modelling perspective. 

1 aHinsche, Marcel1 aIoannou, Marios1 aNietner, Alexander1 aHaferkamp, Jonas1 aQuek, Yihui1 aHangleiter, Dominik1 aSeifert, Jean-Pierre1 aEisert, Jens1 aSweke, Ryan uhttps://arxiv.org/abs/2110.0551701694nas a2200169 4500008004100000245004400041210004400085260001500129520120900144100002801353700002801381700002101409700002001430700001701450700002001467856003701487 2021 eng d00aResource theory of quantum uncomplexity0 aResource theory of quantum uncomplexity c10/21/20213 a

Quantum complexity is emerging as a key property of many-body systems, including black holes, topological materials, and early quantum computers. A state's complexity quantifies the number of computational gates required to prepare the state from a simple tensor product. The greater a state's distance from maximal complexity, or ``uncomplexity,'' the more useful the state is as input to a quantum computation. Separately, resource theories -- simple models for agents subject to constraints -- are burgeoning in quantum information theory. We unite the two domains, confirming Brown and Susskind's conjecture that a resource theory of uncomplexity can be defined. The allowed operations, fuzzy operations, are slightly random implementations of two-qubit gates chosen by an agent. We formalize two operational tasks, uncomplexity extraction and expenditure. Their optimal efficiencies depend on an entropy that we engineer to reflect complexity. We also present two monotones, uncomplexity measures that decline monotonically under fuzzy operations, in certain regimes. This work unleashes on many-body complexity the resource-theory toolkit from quantum information theory.

1 aHalpern, Nicole, Yunger1 aKothakonda, Naga, B. T.1 aHaferkamp, Jonas1 aMunson, Anthony1 aEisert, Jens1 aFaist, Philippe uhttps://arxiv.org/abs/2110.11371